Experiences with Modeling Network Topologies at Multiple Levels of Abstraction

Martin Pool
Xiaoxue Zhao
17th Symposium on Networked Systems Design and Implementation (NSDI)(2020)


Network management is becoming increasingly automated, and automation depends on detailed, explicit representations of data about both the state of a network, and about an operator’s intent for its networks. In particular, we must explicitly represent the desired and actual topology of a network; almost all other network-management data either derives from its topology, constrains how to use a topology, or associates resources (e.g., addresses) with specific places in a topology. We describe MALT, a Multi-Abstraction-Layer Topology representation, which supports virtually all of our network management phases: design, deployment, configuration, operation, measurement, and analysis. MALT provides interoperability across software systems, and its support for abstraction allows us to explicitly tie low-level network elements to high-level design intent. MALT supports a declarative style that simplifies what-if analysis and testbed support. We also describe the software base that supports efficient use of MALT, as well as numerous, sometimes painful lessons we have learned about curating the taxonomy for a comprehensive, and evolving, representation for topology.